PlanetScale vs Neon vs CockroachDB Comparison 2026

In this tutorial, you'll learn about PlanetScale vs Neon vs CockroachDB Comparison 2026. We cover key concepts, practical examples, and best practices to help you understand and apply this topic effectively.

PlanetScale offers MySQL-compatible Serverless with branching and zero-downtime schema changes, Neon separates compute and storage for instant Postgres scaling, and CockroachDB provides globally-distributed SQL with automatic survivability.

At a Glance

Key Differences

• Architecture: PlanetScale uses Vitess (MySQL-compatible) for horizontal Sharding. Neon separates compute and storage — the compute layer spins down when idle, storage persists in S3. CockroachDB replicates data across nodes using the Raft consensus protocol.
• Schema workflow: PlanetScale's branching model is unique — create a branch, make schema changes, diff against production, deploy with revert capability. Neon supports branching from any point in time. CockroachDB uses standard migrations.
• PostgreSQL compatibility: Neon is fully PostgreSQL-compatible. CockroachDB speaks PostgreSQL wire protocol but has some limitations (no triggers, no stored procedures). PlanetScale is MySQL-only.
• Global distribution: CockroachDB is designed for multi-region from day one — it survives entire region failures automatically. PlanetScale supports multi-region reads. Neon adds multi-region in higher tiers.

Side by Side: Connection

PlanetScale

import { connect } from "@planetscale/database";

const conn = connect({
  url: process.env.DATABASE_URL, // mysql://connection string
});

const results = await conn.execute(
  "SELECT * FROM threats WHERE severity = ?",
  ["critical"]
);
console.log(results.rows);

Neon

import { Pool } from "@neondatabase/serverless";

const pool = new Pool({
  connectionString: process.env.DATABASE_URL, // postgres://
});

const { rows } = await pool.query(
  "SELECT * FROM threats WHERE severity = $1",
  ["critical"]
);
console.log(rows);

CockroachDB

import { Pool } from "pg";

const pool = new Pool({
  connectionString: process.env.DATABASE_URL, // postgresql://
});

const { rows } = await pool.query(
  "SELECT * FROM threats WHERE severity = $1",
  ["critical"]
);
console.log(rows);

Expected output (all three):

[
  { id: "1", name: "Emotet", severity: "critical", detected_at: "2026-06-24" },
  { id: "2", name: "Mirai", severity: "critical", detected_at: "2026-06-23" }
]

Side by Side: Schema Branching

PlanetScale

# Create a branch for schema changes
pscale branch create threat-api add-email-field

# Make changes on the branch (schema safe)
pscale shell threat-api add-email-field \
  -- "ALTER TABLE threats ADD COLUMN email TEXT;"

# Diff the branch against production
pscale deploy-request create threat-api add-email-field

# Deploy (zero-downtime, revertable)
pscale deploy-request deploy threat-api <request-id>

Neon

# Create a branch at a specific point in time
neon branch create my-branch --parent main --pg-version 16

# Connect to branch and make changes
neon connection-string my-branch

# Branch contains full data from the point of creation
# Useful for testing migrations against production data

flowchart LR
    subgraph "PlanetScale Branching"
        P["Production DB"] -->|Branch| B1["staging-branch"]
        B1 -->|Schema diff| DPR["Deploy Request"]
        DPR -->|Deploy| P
    end

    subgraph "Neon Branching"
        N["Production DB"] -->|Branch at time| B2["preview-branch"]
        B2 -->|Test migration| M["Migration"]
        M -->|Apply| N
    end

    subgraph "CockroachDB"
        C["Production DB"] -->|Region A| R1["us-east"]
        C -->|Region B| R2["us-west"]
        C -->|Region C| R3["eu-west"]
    end

    style P fill:#dbeafe,stroke:#2563eb
    style B1 fill:#bbf7d0,stroke:#16a34a
    style B2 fill:#fef3c7,stroke:#d97706
    style C fill:#e0e7ff,stroke:#4f46e5

FAQ

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